Ensemble Machine Learning in Python: Random Forest, AdaBoost

Ensemble Methods: Boosting, Bagging, Boostrap, and Statistical Machine Learning for Data Science in Python

4.65 (2636 reviews)

Udemy

platform

English

language

Data Science

What you will learn

Understand and derive the bias-variance decomposition

Understand the bootstrap method and its application to bagging

Understand why bagging improves classification and regression performance

Understand and implement Random Forest

Understand and implement AdaBoost

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Our Verdict

Ensemble Machine Learning in Python: Random Forest, AdaBoost offers a solid educational experience that delves into ensemble methods like boosting and bagging using Python. The course's strengths include updated concepts, ideas, and codes integrated with theoretical explanations, enhancing the learners' understanding of machine learning models. However, a few weaknesses persist: an overwhelming start due to less detail, extended appendix content, and challenging math without adequate layman explanations. Therefore, prospective learners should balance these factors before committing to the course.

What We Liked

Instructor provides updated concepts, ideas, and codes to increase the efficiency of machine learning models
Comprehensive coverage of ensemble methods like bagging, boosting, and random forests
Thorough explanation of bias-variance decomposition at the heart of ensemble algorithms
Practical implementation of boosting, bagging, and random forests from scratch, facilitating understanding of the algorithm

Potential Drawbacks

Theoretical start of the course can be challenging with limited detail and lack of powerful didactic for some concepts
Navigating through the course requires attention due to unrelated appendix content consuming 2.5 hours
Lectures sometimes struggle to keep up with the math, lacking clear explanations in layman's terms for better understanding